Lec 22 - Summary Statistics.ipynb

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   "cells": [

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#Now we'll learn about pandas built-in methods of summarizing data founr in DataFrames\n",

      "import numpy as np\n",

      "from pandas import Series,DataFrame\n",

      "import pandas as pd"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [],

     "prompt_number": 1

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#Let's create a dataframe to work with\n",

      "arr = np.array([[1,2,np.nan],[np.nan,3,4]])\n",

      "dframe1 = DataFrame(arr,index=['A','B'],columns = ['One','Two','Three'])\n",

      "\n",

      "#Show\n",

      "dframe1"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "html": [

        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",

        "<table border=\"1\" class=\"dataframe\">\n",

        "  <thead>\n",

        "    <tr style=\"text-align: right;\">\n",

        "      <th></th>\n",

        "      <th>One</th>\n",

        "      <th>Two</th>\n",

        "      <th>Three</th>\n",

        "    </tr>\n",

        "  </thead>\n",

        "  <tbody>\n",

        "    <tr>\n",

        "      <th>A</th>\n",

        "      <td>  1</td>\n",

        "      <td> 2</td>\n",

        "      <td>NaN</td>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>B</th>\n",

        "      <td>NaN</td>\n",

        "      <td> 3</td>\n",

        "      <td>  4</td>\n",

        "    </tr>\n",

        "  </tbody>\n",

        "</table>\n",

        "</div>"

       ],

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 3,

       "text": [

        "   One  Two  Three\n",

        "A    1    2    NaN\n",

        "B  NaN    3      4"

       ]

      }

     ],

     "prompt_number": 3

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#Let's see the sum() method in action\n",

      "dframe1.sum()"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 4,

       "text": [

        "One      1\n",

        "Two      5\n",

        "Three    4\n",

        "dtype: float64"

       ]

      }

     ],

     "prompt_number": 4

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#Notice how it ignores NaN values"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [],

     "prompt_number": 5

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#We can also over rows instead of columns\n",

      "dframe1.sum(axis=1)"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 6,

       "text": [

        "A    3\n",

        "B    7\n",

        "dtype: float64"

       ]

      }

     ],

     "prompt_number": 6

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#Can also grab min and max values of dataframe\n",

      "dframe1.min()"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 7,

       "text": [

        "One      1\n",

        "Two      2\n",

        "Three    4\n",

        "dtype: float64"

       ]

      }

     ],

     "prompt_number": 7

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#As well as there index\n",

      "dframe1.idxmin()"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 8,

       "text": [

        "One      A\n",

        "Two      A\n",

        "Three    B\n",

        "dtype: object"

       ]

      }

     ],

     "prompt_number": 8

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#Same deal with max, just replace min for max"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [],

     "prompt_number": 9

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#Show\n",

      "dframe1"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "html": [

        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",

        "<table border=\"1\" class=\"dataframe\">\n",

        "  <thead>\n",

        "    <tr style=\"text-align: right;\">\n",

        "      <th></th>\n",

        "      <th>One</th>\n",

        "      <th>Two</th>\n",

        "      <th>Three</th>\n",

        "    </tr>\n",

        "  </thead>\n",

        "  <tbody>\n",

        "    <tr>\n",

        "      <th>A</th>\n",

        "      <td>  1</td>\n",

        "      <td> 2</td>\n",

        "      <td>NaN</td>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>B</th>\n",

        "      <td>NaN</td>\n",

        "      <td> 3</td>\n",

        "      <td>  4</td>\n",

        "    </tr>\n",

        "  </tbody>\n",

        "</table>\n",

        "</div>"

       ],

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 10,

       "text": [

        "   One  Two  Three\n",

        "A    1    2    NaN\n",

        "B  NaN    3      4"

       ]

      }

     ],

     "prompt_number": 10

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#Can also do an accumulation sum\n",

      "dframe1.cumsum()"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "html": [

        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",

        "<table border=\"1\" class=\"dataframe\">\n",

        "  <thead>\n",

        "    <tr style=\"text-align: right;\">\n",

        "      <th></th>\n",

        "      <th>One</th>\n",

        "      <th>Two</th>\n",

        "      <th>Three</th>\n",

        "    </tr>\n",

        "  </thead>\n",

        "  <tbody>\n",

        "    <tr>\n",

        "      <th>A</th>\n",

        "      <td>  1</td>\n",

        "      <td> 2</td>\n",

        "      <td>NaN</td>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>B</th>\n",

        "      <td>NaN</td>\n",

        "      <td> 5</td>\n",

        "      <td>  4</td>\n",

        "    </tr>\n",

        "  </tbody>\n",

        "</table>\n",

        "</div>"

       ],

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 11,

       "text": [

        "   One  Two  Three\n",

        "A    1    2    NaN\n",

        "B  NaN    5      4"

       ]

      }

     ],

     "prompt_number": 11

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#A very useful feature is describe, which provides summary statistics\n",

      "dframe1.describe()"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "html": [

        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",

        "<table border=\"1\" class=\"dataframe\">\n",

        "  <thead>\n",

        "    <tr style=\"text-align: right;\">\n",

        "      <th></th>\n",

        "      <th>One</th>\n",

        "      <th>Two</th>\n",

        "      <th>Three</th>\n",

        "    </tr>\n",

        "  </thead>\n",

        "  <tbody>\n",

        "    <tr>\n",

        "      <th>count</th>\n",

        "      <td>  1</td>\n",

        "      <td> 2.000000</td>\n",

        "      <td>  1</td>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>mean</th>\n",

        "      <td>  1</td>\n",

        "      <td> 2.500000</td>\n",

        "      <td>  4</td>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>std</th>\n",

        "      <td>NaN</td>\n",

        "      <td> 0.707107</td>\n",

        "      <td>NaN</td>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>min</th>\n",

        "      <td>  1</td>\n",

        "      <td> 2.000000</td>\n",

        "      <td>  4</td>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>25%</th>\n",

        "      <td>  1</td>\n",

        "      <td> 2.250000</td>\n",

        "      <td>  4</td>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>50%</th>\n",

        "      <td>  1</td>\n",

        "      <td> 2.500000</td>\n",

        "      <td>  4</td>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>75%</th>\n",

        "      <td>  1</td>\n",

        "      <td> 2.750000</td>\n",

        "      <td>  4</td>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>max</th>\n",

        "      <td>  1</td>\n",

        "      <td> 3.000000</td>\n",

        "      <td>  4</td>\n",

        "    </tr>\n",

        "  </tbody>\n",

        "</table>\n",

        "</div>"

       ],

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 12,

       "text": [

        "       One       Two  Three\n",

        "count    1  2.000000      1\n",

        "mean     1  2.500000      4\n",

        "std    NaN  0.707107    NaN\n",

        "min      1  2.000000      4\n",

        "25%      1  2.250000      4\n",

        "50%      1  2.500000      4\n",

        "75%      1  2.750000      4\n",

        "max      1  3.000000      4"

       ]

      }

     ],

     "prompt_number": 12

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "# We can also get information on correlation and covariance\n",

      "\n",

      "#For more info on correlation and covariance, check out the videos below!"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [],

     "prompt_number": 13

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "from IPython.display import YouTubeVideo\n",

      "# For more information about Covariaance and Correlation\n",

      "# Check out these great videos!\n",

      "# Video credit: Brandon Foltz.\n",

      "\n",

      "#CoVariance\n",

      "YouTubeVideo('xGbpuFNR1ME')"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "html": [

        "\n",

        "        <iframe\n",

        "            width=\"400\"\n",

        "            height=300\"\n",

        "            src=\"https://www.youtube.com/embed/xGbpuFNR1ME\"\n",

        "            frameborder=\"0\"\n",

        "            allowfullscreen\n",

        "        ></iframe>\n",

        "        "

       ],

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 14,

       "text": [

        "<IPython.lib.display.YouTubeVideo at 0xa9a1a58>"

       ]

      }

     ],

     "prompt_number": 14

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#Correlation\n",

      "YouTubeVideo('4EXNedimDMs')"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "html": [

        "\n",

        "        <iframe\n",

        "            width=\"400\"\n",

        "            height=300\"\n",

        "            src=\"https://www.youtube.com/embed/4EXNedimDMs\"\n",

        "            frameborder=\"0\"\n",

        "            allowfullscreen\n",

        "        ></iframe>\n",

        "        "

       ],

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 15,

       "text": [

        "<IPython.lib.display.YouTubeVideo at 0xa9a1940>"

       ]

      }

     ],

     "prompt_number": 15

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#Now lets check correlation and covariance on some stock prices!\n",

      "\n",

      "#Pandas can get info off the web\n",

      "import pandas.io.data as pdweb\n",

      "\n",

      "#Set datetime for date input\n",

      "import datetime\n",

      "\n",

      "#Get the closing prices\n",

      "\n",

      "prices = pdweb.get_data_yahoo(['CVX','XOM','BP'], \n",

      "                               start=datetime.datetime(2010, 1, 1), \n",

      "                               end=datetime.datetime(2013, 1, 1))['Adj Close']\n",

      "#Show preview\n",

      "prices.head()"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "html": [

        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",

        "<table border=\"1\" class=\"dataframe\">\n",

        "  <thead>\n",

        "    <tr style=\"text-align: right;\">\n",

        "      <th></th>\n",

        "      <th>BP</th>\n",

        "      <th>CVX</th>\n",

        "      <th>XOM</th>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>Date</th>\n",

        "      <th></th>\n",

        "      <th></th>\n",

        "      <th></th>\n",

        "    </tr>\n",

        "  </thead>\n",

        "  <tbody>\n",

        "    <tr>\n",

        "      <th>2010-01-04</th>\n",

        "      <td> 46.97</td>\n",

        "      <td> 66.17</td>\n",

        "      <td> 60.26</td>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>2010-01-05</th>\n",

        "      <td> 47.30</td>\n",

        "      <td> 66.63</td>\n",

        "      <td> 60.50</td>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>2010-01-06</th>\n",

        "      <td> 47.55</td>\n",

        "      <td> 66.64</td>\n",

        "      <td> 61.02</td>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>2010-01-07</th>\n",

        "      <td> 47.53</td>\n",

        "      <td> 66.39</td>\n",

        "      <td> 60.83</td>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>2010-01-08</th>\n",

        "      <td> 47.64</td>\n",

        "      <td> 66.51</td>\n",

        "      <td> 60.59</td>\n",

        "    </tr>\n",

        "  </tbody>\n",

        "</table>\n",

        "</div>"

       ],

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 16,

       "text": [

        "               BP    CVX    XOM\n",

        "Date                           \n",

        "2010-01-04  46.97  66.17  60.26\n",

        "2010-01-05  47.30  66.63  60.50\n",

        "2010-01-06  47.55  66.64  61.02\n",

        "2010-01-07  47.53  66.39  60.83\n",

        "2010-01-08  47.64  66.51  60.59"

       ]

      }

     ],

     "prompt_number": 16

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#Now lets get the volume trades\n",

      "\n",

      "volume = pdweb.get_data_yahoo(['CVX','XOM','BP'], \n",

      "                               start=datetime.datetime(2010, 1, 1), \n",

      "                               end=datetime.datetime(2013, 1, 1))['Volume']\n",

      "\n",

      "#Show preview\n",

      "volume.head()"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "html": [

        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",

        "<table border=\"1\" class=\"dataframe\">\n",

        "  <thead>\n",

        "    <tr style=\"text-align: right;\">\n",

        "      <th></th>\n",

        "      <th>BP</th>\n",

        "      <th>CVX</th>\n",

        "      <th>XOM</th>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>Date</th>\n",

        "      <th></th>\n",

        "      <th></th>\n",

        "      <th></th>\n",

        "    </tr>\n",

        "  </thead>\n",

        "  <tbody>\n",

        "    <tr>\n",

        "      <th>2010-01-04</th>\n",

        "      <td> 3956100</td>\n",

        "      <td> 10173800</td>\n",

        "      <td> 27809100</td>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>2010-01-05</th>\n",

        "      <td> 4109600</td>\n",

        "      <td> 10593700</td>\n",

        "      <td> 30174700</td>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>2010-01-06</th>\n",

        "      <td> 6227900</td>\n",

        "      <td> 11014600</td>\n",

        "      <td> 35044700</td>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>2010-01-07</th>\n",

        "      <td> 4431300</td>\n",

        "      <td>  9626900</td>\n",

        "      <td> 27192100</td>\n",

        "    </tr>\n",

        "    <tr>\n",

        "      <th>2010-01-08</th>\n",

        "      <td> 3786100</td>\n",

        "      <td>  5624300</td>\n",

        "      <td> 24891800</td>\n",

        "    </tr>\n",

        "  </tbody>\n",

        "</table>\n",

        "</div>"

       ],

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 17,

       "text": [

        "                 BP       CVX       XOM\n",

        "Date                                   \n",

        "2010-01-04  3956100  10173800  27809100\n",

        "2010-01-05  4109600  10593700  30174700\n",

        "2010-01-06  6227900  11014600  35044700\n",

        "2010-01-07  4431300   9626900  27192100\n",

        "2010-01-08  3786100   5624300  24891800"

       ]

      }

     ],

     "prompt_number": 17

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#Lets get the return\n",

      "rets = prices.pct_change()"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [],

     "prompt_number": 18

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#Get the correlation of the stocks\n",

      "corr = rets.corr"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [],

     "prompt_number": 23

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#Lets see the prices over time to get a very rough idea of the correlation between the stock prices\n",

      "prices.plot()"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 24,

       "text": [

        "<matplotlib.axes._subplots.AxesSubplot at 0x1aa126a0>"

       ]

      },

      {

       "metadata": {},

       "output_type": "display_data",

       "png": 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c/OxMwd1h0alcOeYSlpgX8p9970lunKXpi0gMiUer0hCvjyW3Nl/KrgzThkpJ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       "text": [

        "<matplotlib.figure.Figure at 0x1aa085f8>"

       ]

      }

     ],

     "prompt_number": 24

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "import seaborn as sns\n",

      "import matplotlib.pyplot as plt\n",

      "%matplotlib inline\n",

      "\n",

      "#As expected pretty strong correlations with eachother\n",

      "sns.heatmap(rets.corr())\n",

      "\n",

      "#We'll learn much more about seaborn later!"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 20,

       "text": [

        "<matplotlib.axes._subplots.AxesSubplot at 0x18b63a58>"

       ]

      },

      {

       "metadata": {},

       "output_type": "display_data",

       "png": 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       "text": [

        "<matplotlib.figure.Figure at 0x18b63550>"

       ]

      }

     ],

     "prompt_number": 20

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "# We can also check for unique values and their counts \n",

      "\n",

      "#For example\n",

      "ser1 = Series(['w','w','x', 'y', 'z' ,'w' ,'w' ,'x' ,'x' ,'y' ,'a' ,'z' ])\n",

      "\n",

      "#Show\n",

      "ser1"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 2,

       "text": [

        "0     w\n",

        "1     w\n",

        "2     x\n",

        "3     y\n",

        "4     z\n",

        "5     w\n",

        "6     w\n",

        "7     x\n",

        "8     x\n",

        "9     y\n",

        "10    a\n",

        "11    z\n",

        "dtype: object"

       ]

      }

     ],

     "prompt_number": 2

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#Grab the unique values\n",

      "ser1.unique()"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 3,

       "text": [

        "array(['w', 'x', 'y', 'z', 'a'], dtype=object)"

       ]

      }

     ],

     "prompt_number": 3

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#Now get the count of the unique values\n",

      "ser1.value_counts()"

     ],

     "language": "python",

     "metadata": {},

     "outputs": [

      {

       "metadata": {},

       "output_type": "pyout",

       "prompt_number": 4,

       "text": [

        "w    4\n",

        "x    3\n",

        "z    2\n",

        "y    2\n",

        "a    1\n",

        "dtype: int64"

       ]

      }

     ],

     "prompt_number": 4

    },

    {

     "cell_type": "code",

     "collapsed": false,

     "input": [

      "#Next we'll learn how to best deal with missing data!"

     ],

     "language": "python",

     "metadata": {},

     "outputs": []

    }

   ],

   "metadata": {}

  }

 ]

}